A Network Intrusion Risk Identification Based on Deep Learning
LI Xiangyuan
XU Shengchao
LYU Junmin
Abstract：A network intrusion risk identification method based on deep learning is proposed.Firstly,it preprocesses the col-lected network data,extracts statisticals and power spectral features of the data,and then integrate is the extracted features.The en-tropy value of the integrated features is calculated to determine abnormal data in the network data.A loss function is set,regulariza-tion terms to construct the loss function is introduced to reduce model complexity,the extracted features are screened,and the corre-sponding risk factors are calculated.Combined with the probability of risk factor occurrence,it calculates the intrusion risk value of network data and divide the network intrusion risk level.The experimental results show that the designed identification method has an average misidentification rate of 7.2％and high identification accuracy in practical applications.Meanwhile,the computational ef-ficiency of the designed identification method consistently remains above 90％,demonstrating higher efficiency and faster identifica-tion of network intrusion risks.
Keywords：Internetnetwork intrusionnetwork riskrisk identificationnetwork security
Publication Date：2025-12-20
Online Publishing Date：2026-03-23(First online date of this platform, not the publication date of the document)
Pages：6( 118-123 )